DOCSLIB.ORG

Susan Murphy's Slides

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Susan Murphy's Slides 85 min. What is a JITAI -- how it relates to multi-component interventions and to AIs Motivation for JITAI What is MRT (basic features) The connection between MRT and MOST, Factorial designs and SMART SA Murphy 1 three available stress-regulation apps (headspace; mood-surfing; thought distancing; and cognitive restructuring). 2 Sedentary adults Next study will be with adults who are at high risk for heart attack 3 4 5 Monitoring, individualizing, delivering 6 The same elements that we used to describe an adaptive intervention can be used to describe JITAISs, only that now all these elements are in-the-moment. Dynamic Models of Behavior for Just-in-Time Adaptive Interventions Donna Spruijt-Metz, University of Southern California Wendy Nilsen, National Institutes of Health PERVASIVE computing, 1536-1268/14/2014 IEEE Nahum-Shani, I., Smith, S.N. Spring, B.J., Collins, L.M., Witkiewitz, K., Tewari, A., & Murphy, S.A. (2016). Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Annals of Behavioral Medicine . doi:10.1007/s12160-016-9830-8, PMCID: PMC5364076 Different terms have been used in various fields to describe a JITAI, including dynamic tailoring, intelligent real-time therapy, and dynamically and individually tailored EMI 7 Development and evaluation of a mobile intervention for heavy drinking and smoking among college students. Witkiewitz, Katie; Desai, Sruti A.; Bowen, Sarah; Leigh, Barbara C.; Kirouac, Megan; Larimer, Mary E. Psychology of Addictive Behaviors, Vol 28(3), Sep 2014, 639-650 8 Quote from paper: whenever 30 min of nearly uninterrupted computer activity was recorded, a short text message (SMS) containing a hyperlink was sent to the participant’s smart phone. When participants clicked on this hyperlink, they were shown a message persuading them to be more active. Although all messages contained the same general advice, this advice was phrased in various ways, using four different persuasive strategies. The four strategies are a subset of the six social influence strategies defined by Cialdini [22]. Dantzig, S., Geleijnse, G., & Halteren, A. T. (2013). Toward a persuasive mobile application to reduce sedentary behavior. Personal and ubiquitous computing, 17 (6), 1237-1246. 9 The same elements that compose an adaptive intervention, also compose a JITAI. However, in a JITAI these elements are in-the-moment – they can occur at any moment. 10 11 12 In MD2K smoking study the distal outcome might be time to relapse. 13 Likely multiple proximal outcomes In MD2k study the proximal outcome might be stress over next x minutes. 14 Intervention options are typically designed to impact distal outcome via the proximal outcome. In MD2K study the intervention option might be a recommendation to access one of the three stress-regulation apps (headspace; mood-surfing; and ?) residing on the smartphone vs. no recommendation. Intervention options in JITAIs include types of support, sources of support (e.g., automated sources, social sources); and modes of support delivery. Recommendations Reach out recommendation (contact a friend) 15 Behavioral strategies (exercise; stay in locations that are supportive of change) Cognitive strategies (relaxation; reframing) Motivational messages (reasons for behavior change; barriers for change); Setting goals; modifying goals Feedback (often with visualization: fish; flower; garden) Distractions (game, music, etc.) Michel Klein et al. have a nice review of health behavior change theories used to inform EMIs Klein, M., Mogles, N., & van Wissen, A. (2011). Why won’t you do what’s good for you? Using intelligent support for behavior change. In Human Behavior Understanding (pp. 104-115). Springer Berlin Heidelberg. West, R., & Michie, S. (2016). A Guide to Development and Evaluation of Digital Interventions in Healthcare. London: Silverback In MD2K smoking study tailoring variables might be current classification of stressed or not and location (home, work), time of day (before work, during work, after work). Also weather. indicate risk or vulnerability. --internal risk factors , external risk factors: behaviors, social context, geographical location, When user ignores assessment requests or ignores intervention 16 Recall that a decision point is the time in which we need to make critical decisions about the intervention options based on patient information. decision points can result in the “do nothing intervention option,” hence a decision point every 3 minutes does not imply an intervention every 3 minutes. 17 The decision rules are constructed with the aim to impact a proximal outcome. We can use the data from the micro-randomized study along with behavioral science to construct decision rules. 18 19 20 21 Make a break –everyone stretches! 22 Sedentary adults Next study will be with adults who are at high risk of an adverse cardiac event 23 three available stress-regulation apps (headspace; mood-surfing; thought distancing; and cognitive restructuring). Currently, AutoSense consists of an arm band with four wireless sensors and a chestband with six wireless sensors. All the ten sensors are integrated onto an embedded platform called “mote,” a tiny self-contained, battery-powered computer with a wireless radio that can host multiple sensors, collect and process data from them using customized algorithms, and communicate on secure wireless channels. The chestband consists of 2-lead Electrocardiogram (ECG), galvanic skin response (GSR), respiratory inductive plethysmograph (RIP) band for robust measurement of respiration, skin temperature, ambient temperature, and a 3-axis accelerometer. Accelerometers help classify physical activities, estimate their intensities, and help remove motion artifacts from the measurements of ECG, RIP, and GSR. The armband consists of WrisTAS alcohol sensor from Giner Inc., accelerometers, GSR, and temperature sensors. All sensors communicate wirelessly with a smart phone. Sensors on the phone (e.g., GPS, microphone) complement those on the body. The phone also acts as a local server for heavier computation and storage. A person is available if he/she (1) is wearing autosense (our understanding is that autosense will be worn up to 16 hours a day, participants will not wear it when they 24 sleep or when in the shower; if the person is not wearing Autosense, no data will be collected and recommendations will not be pushed); (2) did not receive a message in the past x minutes; and (3) is not driving a car. SA Murphy 25 The momentary times were selected because these times are the times at which most people are able to be active Pre-morning commute, mid-day, mid-afternoon, evening commute, after dinner. Another example: The phone software monitors a risk measure at regular time intervals and if the risk measures hits a criterion then a treatment is provided. Can include time of day or day of week and present weather. Some Treatment types behavioral, cognitive, motivational, social, self-monitoring, information Tailored to time of day, location, weather outside, weekday The location of the like button biases against the person hitting like. The snozz button turns off the momentary lock screen recommendations for x hours. Occurs up to 5 times per day SA Murphy 29 Frequently the actions are primarily designed to have a near-term effect on the individual. E.g. Help then manage current craving/stress, help them manage or be aware of the impact of their social setting on their craving/stress 31 32 33 34 J=42*5=210 momentary randomizations 35 36 Availability is measured prior to randomization. Adherence is measured after randomization. Adherence (i.e. compliance) is very different from availability. Suppose a person is available at a decision point. However the phone is in their purse across the room. So they don’t hear whether the phone pings/ see the lockscreen light up. This person is non-adherent at this decision point. Primary analyses will be intention-to-treat and thus will average over non-adherence. SA Murphy 37 Marginal over randomization (and effects thereof), conditional on those who are available. The group who are available is a selected group of people likely depending on the intervention dose they experienced up to time j. This intervention dose may have caused burden, may have caused learning. SA Murphy 38 In heartsteps beta(t) is the effect of tailored activity suggestion on next 30min step count. Delayed effects which are akin to higher order interactions would be investigated in secondary analyses SA Murphy 39 Instead of a sparsity bet, we place a smoothness bet We are not assuming that the main effect has a quadratic form Since the test statistic for the main effect does not depend on time of day, we are averaging any variation in main effect across the occasions during the day (recall we are sizing the study; a primary analysis might be a little more complex and in secondary data analyses one would likely estimate and test if the effect varies by time of day and/or varies by day in study). SA Murphy 40 SA Murphy 41 The contrasts become within person contrasts due to smoothness over time in the targeted quadratic alternative. If the main effect at each time point were to be estimated separately then it would be like a two arm study at each time j. SA Murphy 42 SA Murphy 43 Meaningful increase in stepcount is 1000/day Usual std is 2000/day Roughly a standardized treatment effect of 200/666= .3 Sample size calculators https://pengliao.shinyapps.io/mrt-calculator/ R package https://cran.r-project.org/web/packages/MRTSampleSize/index.html SA Murphy 44 45 46 47 48 Single-patient trials or individual-patient trials or single-case experiments 49 50 ABBA trials, single case designs, quasi-experimental Design and Implementation of N-of-1 Trials: A users’ guide https://www.effectivehealthcare.ahrq.gov/ehc/products/534/1844/n-1-trials-report- 130213.pdf Kravitz RL, Duan N, eds, and the DEcIDE Methods Center N-of-1 Guidance Panel (Duan N, Eslick I, Gabler NB, Kaplan HC, Kravitz RL, Larson EB, Pace WD, Schmid CH, Sim I, Vohra S).
Load more

Recommended publications
  • STEVE AMIREAULT, Ph.D
    STEVE AMIREAULT, Ph.D. 800 West Stadium Avenue, West Lafayette, IN, 47907, USA Phone: +1 (765)-496-0568; Email: [email protected] Website: Physical Activity Psychology Lab AREA OF EXPERTISE Epidemiology, exercise psychology, health and well-being, maintenance of health behaviors, motivation, physical activity, public health, questionnaire development and refinement, systematic review and meta-analysis, validation studies. CURRENT EMPLOYMENT 2015 - Present Assistant Professor Department of Health and Kinesiology College of Health and Human Sciences Purdue University Lambert Fieldhouse, Room 311A 800 West Stadium Avenue West Lafayette, IN 47907-2046 EDUCATION Note: Title in bracket ‘[…]’ = English translation. 2013-2015 Postdoctoral Fellowship Faculty of Kinesiology & Physical Education, University of Toronto, Toronto, ON, Canada. Supervisor: Dr. Catherine M. Sabiston (Canada Research Chair in Physical Activity and Mental Health) 2013-2015 Faculty of Arts and Science, Department of Psychology, Concordia University, Montréal, QC, Canada. Co-supervisor: Dr. Carsten Wrosch (Canada Research Chair in Aging and Health) 2009-2013 Doctor of Philosophy (Ph.D.) in Kinesiology Faculty of Medicine, Department of Kinesiology, Université Laval, Quebec City, QC, Canada. Supervisor: Dr. Gaston Godin (Canada Research Chair in Behaviour and Health). Dissertation Title : Effet d’une intervention visant à favoriser le maintien de la pratique de l’activité physique [Effectiveness of an intervention to promote the maintenance of physical activity]. 2005-2007 Master in Sciences (M.Sc.) in Kinesiology Faculty of Medicine, Division of Kinesiology, Université Laval, Quebec City, QC, Canada. Supervisor: Dr. Gaston Godin (Canada Research Chair on Behaviour and Health). Co-supervisor: Dr. Louis Pérusse. 2002-2005 Bachelor of Science (B.Sc.) Kinesiology Faculty of Medicine, Division of Kinesiology, Université Laval, Quebec City, QC, Canada.
    [Show full text]
  • IMS Presidential Address(Es) Each Year, at the End of Their Term, the IMS CONTENTS President Gives an Address at the Annual Meeting
    Volume 50 • Issue 6 IMS Bulletin September 2021 IMS Presidential Address(es) Each year, at the end of their term, the IMS CONTENTS President gives an address at the Annual Meeting. 1 IMS Presidential Addresses: Because of the pandemic, the Annual Meeting, Regina Liu and Susan Murphy which would have been at the World Congress in Seoul, didn’t happen, and Susan Murphy’s 2–3 Members’ news : Bin Yu, Marloes Maathuis, Jeff Wu, Presidential Address was postponed to this year. Ivan Corwin, Regina Liu, Anru The 2020–21 President, Regina Liu, gave her Zhang, Michael I. Jordan Address by video at the virtual JSM, which incorpo- 6 Profile: Michael Klass rated Susan’s video Address. The transcript of the two addresses is below. 8 Recent papers: Annals of 2020–21 IMS President Regina Liu Probability, Annals of Applied Probability, ALEA, Probability Regina Liu: It’s a great honor to be the IMS President, and to present this IMS and Mathematical Statistics Presidential address in the 2021 JSM. Like many other things in this pandemic time, this year’s IMS Presidential Address will be also somewhat different from those in the 10 IMS Awards past. Besides being virtual, it will be also given jointly by the IMS Past President Susan 11 IMS London meeting; Olga Murphy and myself. The pandemic prevented Susan from giving her IMS Presidential Taussky-Todd Lecture; Address last year. In keeping with the IMS tradition, I invited Susan to join me today, Observational Studies and I am very pleased she accepted my invitation. Now, Susan will go first with her 12 Sound the Gong: Going 2020 IMS Presidential Address.
    [Show full text]
  • STATISTICS in the DATA SCIENCE ERA: a Symposium to Celebrate 50 Years of Statistics at the University of Michigan
    STATISTICS IN THE DATA SCIENCE ERA: A Symposium to Celebrate 50 Years of Statistics at the University of Michigan September 20-21, 2019 Rackham Graduate School 915 E. Washington Street Ann Arbor, MI 48109 A Symposium to Celebrate 50 Years of Statistics at the University of Michigan 1 PROGRAM Friday, September 20, 2019 Saturday, September 21, 2019 9:30-10:30 ...............Coffee and Registration 8:30-9:00 ................ Coffee 10:30-10:50 ..............Opening Remarks Invited Session 2 Chair: Snigdha Panigrahi, University of Michigan Xuming He and Dean Anne Curzan 9:00-9:30 ................ Adam Rothman, University of Minnesota Shrinking Characteristics of Precision Matrix Keynote Session 1 Chair: Ambuj Tewari, University of Michigan Estimators 10:50-11:50 ...............Susan Murphy, Harvard University Online Experimentation with Learning Algorithms 9:30-10:00 ............... Bodhisattva Sen, Columbia University in a Clinical Trial Multivariate Rank-based Distribution-free Nonparametric Testing using Measure 11:50-12:00 ...............Group Photo Transportation PROGRAM 12:00-1:30 ................Lunch and Poster Session 10:00-10:30 ............. Min Qian, Columbia University Personalized Policy Learning using Longitudinal University of Michigan Invited Session 1 Chair: Ziwei Zhu, Mobile Health Data 1:30-2:00 .................Sumanta Basu, Cornell University Large Spectral Density Matrix Estimation by 10:30-11:00 .............. Coffee Break Thresholding Invited Session 3 Chair: Kean Ming Tan, University of Michigan 2:00-2:30 .................Anindya Bhadra, Purdue University 11:00-11:30 ............... Ali Shojaie, University of Washington Horseshoe Regularization for Machine Learning in Incorporating Auxiliary Information into Learning Complex and Deep Models Directed Acyclic Graphs 2:30-3:00 .................Coffee Break 11:30-12:00 ..............
    [Show full text]
  • Smarts: Part I
    SMARTs: Part I Eric B. Laber Department of Statistics, North Carolina State University April 2019 SAMSI Warm up part I: quiz! I Discuss with your stat buddy: I What is a clinical trial? What's the best way to quantify the shame you should feel if you don't know? I What is a power calculation? I What are common complicating statistical issues associated with clinical trials? I True or false I Sequential Hierarchically Assigned Randomization Trials are the gold standard design for estimation and evaluation of treatment regimes. I Susan Murphy, who authored several seminal papers on sequential clinical trial design, is affectionately known as `Smurphy' to her friends and colleagues. I The legend of Santa Clause may be partially based on Siberian Shamans consuming psychedelic mushrooms with reindeer. 1 / 64 Warm up: quiz! I Discuss with your stat buddy: I What is a clinical trial? What's the best way to quantify the shame you should feel if you don't know? I What is a power calculation? I What are common complicating statistical issues associated with clinical trials? I True or false I Sequential Hierarchically Assigned Randomization Trials are the gold standard design for estimation and evaluation of treatment regimes. I Susan Murphy, who authored several seminal papers on sequential clinical trial design, is affectionately known as `Smurphy' to her friends and colleagues. I The legend of Santa Clause may be partially based on Siberian Shamans consuming psychedelic mushrooms with reindeer.1 1https://www.npr.org/2010/12/24/132260025/did-shrooms-send-santa-
    [Show full text]
  • Optimal Dynamic Treatment Regimes and Partial Welfare Ordering∗
    Optimal Dynamic Treatment Regimes and Partial Welfare Ordering∗ Sukjin (Vincent) Han Department of Economics University of Bristol First Draft: August 8, 2019 This Draft: May 31, 2021 Abstract Dynamic treatment regimes are treatment allocations tailored to heterogeneous in- dividuals. The optimal dynamic treatment regime is a regime that maximizes counter- factual welfare. We introduce a framework in which we can partially learn the optimal dynamic regime from observational data, relaxing the sequential randomization as- sumption commonly employed in the literature but instead using (binary) instrumental variables. We propose the notion of sharp partial ordering of counterfactual welfares with respect to dynamic regimes and establish mapping from data to partial ordering via a set of linear programs. We then characterize the identified set of the optimal regime as the set of maximal elements associated with the partial ordering. We relate the notion of partial ordering with a more conventional notion of partial identifica- tion using topological sorts. Practically, topological sorts can be served as a policy menu for a policymaker. We apply our method to understand returns to schooling and post-school training as a sequence of treatments by combining data from multiple sources. The framework of this paper can be used beyond the current context, e.g., in establishing rankings of multiple treatments or policies across different counterfactual scenarios. ∗For helpful comments and discussions, the author is grateful to Donald Andrews, Isaiah
    [Show full text]
  • Copyright by Susan Laura Murphy 2019
    Copyright by Susan Laura Murphy 2019 The Dissertation Committee for Susan Laura Murphy certifies that this is the approved version of the following dissertation: An Evaluation of the Feedback Report for the Preventive Resources Inventory APPROVED BY SUPERVISING COMMITTEE: Christopher J. McCarthy, supervisor Karen D. French Timothy Z. Keith Richard Lambert Delida Sanchez An Evaluation of the Feedback Report for the Preventive Resources Inventory by Susan Laura Murphy Dissertation Presented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy The University of Texas at Austin August 2019 Abstract An Evaluation of the Feedback Report for the Preventive Resources Inventory Susan Laura Murphy, Ph.D. The Univeristy of Texas at Austin, 2019 Supervisor: Christopher J. McCarthy Research on how individuals cope with stress has spanned numerous academic and scientific disciplines, including the fields of counseling and psychology. Investigations have more recently focused on preventive coping, or the coping strategies used by individuals to manage existing stressors and prepare for future demands. The Preventive Resource Inventory (PRI) was developed to assess coping resources for mitigating or preventing stress, rather than withstanding it. The PRI was recently revised to reflect more current theoretical perspectives in stress and coping research, including the influence of positive psychology. This revision process involved developing and testing new items and later conducting a factor analysis to create an updated measure. The present study used the updated version of the PRI to assess the utility of a feedback report for PRI users.
    [Show full text]
  • Investigation of 2 Types of Self-Administered Acupressure for Persistent Cancer-Related Fatigue in Breast Cancer Survivors
    1 ACUPRESSURE FOR PERSISTENT CANCER RELATED FATIGUE 2 3 Principal Investigator: 4 Suzanna M. Zick, ND, MPH 5 24 Frank Lloyd Wright Drive 6 P.O. Box 375 Lobby M 7 Ann Arbor, MI 48106 8 Ph: 734-998-9553 9 Fax 734-998-6900 10 11 Co-Principal Investigator: 12 Richard E. Harris, PhD 13 24 Frank Lloyd Wright Drive 14 P.O. Box 375 Lobby M 15 Ann Arbor, MI 48106 16 Ph: 734-998-6996 17 Fax 734-998-6900 18 19 Co-Investigators: 20 Ananda Sen, PhD 21 J. Todd Arnedt, PhD 22 Susan Murphy, ScD, OTR 23 Gwen Wyatt, PhD, MSN, RN, FAAN 24 Brad Foerster, MD 25 26 Study Coordinators: 27 Vinita Verma / Kevin Shrestha 28 Ph: 734-998-0016 29 Fax: 734-998-6900 30 31 Project Manager 32 Benjamin Wright 33 Ph: 734-998-0031 34 Fax: 734-998-6900 35 36 University of Michigan Health Systems, Ann Arbor, MI 37 Approved by UM IRBMED: HUM00038428 38 39 Current protocol submission and version number: 40 Version 12 6/2/2014 41 42 Date of previous submission dates and version numbers: 43 Version 11 4/3/2014 44 Version 10 6/14/2013 45 Version 9 11/29/2012 46 Version 8 9/25/2012 47 Version 7 3/28/2012 48 Version 6 10/19/2011 49 Version 5 5/20/2011 50 Version 4 01/26/2011 51 Version 3 10/11/2010 52 Version 2 8/25/2010 53 Version 1 3/12/2010 Downloaded From: https://jamanetwork.com/ on 09/24/2021 Acupressure for Persistent Cancer Related Fatigue Study Protocol: Version 12 54 55 STUDY SCHEMA 56 57 ACUPRESSURE FOR PERSISTENT CANCER RELATED FATIGUE 58 59 PI: Suzanna M.
    [Show full text]
  • Dynamic Treatment Regimens
    Dynamic Treatment Regimens Bibhas Chakraborty Centre for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore [email protected] Innovation in Clinical Trial Methodology Workshop Murdoch Childrens Research Institute Melbourne May 20, 2015 1 / 45 Personalized Medicine Believed by many as the future of medicine ... Source: http://www.personalizedmedicine.com/ Often refers to tailoring by genetic profile, but it’s also common to personalize based on more “macro” level characteristics, some of which are time-varying Personalized Medicine Paradigm shift from “one size fits all” to individualized, patient-centric care – Can address inherent heterogeneity across patients – Can also address variability within patient, over time – Can increase patient compliance, thus increasing the chance of treatment success – Likely to reduce the overall cost of health care 3 / 45 Personalized Medicine: Big Methodological Questions – How to decide on the optimal treatment for an individual patient? – How to make these treatment decisions evidence-based or data-driven? This is where Statistics can make a difference! 4 / 45 Outline 1 Dynamic Treatment Regimens (Regimes): What and Why? 2 Sequential Multiple Assignment Randomized Trial (SMART) Design SMART Design: What and Why? SMART Design Principles, Primary Hypothesis and Sample Size 3 Secondary Analysis of SMART: Finding Optimal DTR 4 Discussion 5 / 45 Dynamic Treatment Regimens (Regimes): What and Why? Dynamic Treatment Regimens Consider personalized management of chronic disorders
    [Show full text]
  • Snehalata Huzurbazar Joins SAMSI As Deputy Director
    Volume 5 o nf .i .info Issue 1 Spring 2012 e Institut Sciences Mathematical Applied and Statistical the of Newsletter The The Newsletter of the Statistical and Applied Mathematical Sciences Institute Snehalata Huzurbazar Joins SAMSI as Deputy Director Snehalata Huzurbazar has accepted the position of deputy director of SAMSI, starting July 9. She will be at SAMSI for two years, while taking a leave of absence from the University of Wyoming. Snehalata is replacing Pierre Gremaud, who is finish- ing his fourth year at SAMSI. In her new position, Snehalata will help administer the SAMSI programs and will help to develop future programs. She will also be involved with the education and outreach efforts and will work on staff and personnel issues. Huzurbazar will be a part of the directorate, which comprises the director, three part-time associate directors and the deputy director. Many people may remember Snehalata who was at SAMSI last spring when she was participating in the Analysis of Object- Oriented Data (AOD) program. Her infectious laugh and beauti- ful smile brightened up the hallways of SAMSI. She was very in- volved with the Trees and Dynamics working groups from AOD. Snehalata said she comes from a statistics family. “My father (V. S. Huzurbazar) was a statistician, and Harold Jeffreys’ only statistics student, who started the Mathematics and Statistics Department at the University of Poona in India,” she noted. He Snehalata and her daughter, Asha, taking a hike. was a Fulbright scholar visiting professor at Iowa State when she was born. Her sister, Aparna Huzurbazar is a statistician at Los Institute of Arctic and Alpine Research in Boulder, Colorado.
    [Show full text]
  • Discussion on the Paper by Murphy 355 Directed Acyclic Graphs
    Discussion on the Paper by Murphy 355 directed acyclic graphs. In Proc. 13th Conf. Uncertainty in Artificial Intelligence (eds D. Geiger and P. Shenoy), pp. 409–442. San Francisco: Morgan Kaufmann. Rubin, D. B. (1978) Bayesian inference for causal effects: the role of randomization. Ann. Statist., 6, 34–58. (1986) Which ifs have causal answers. J. Am. Statist. Ass., 81, 961–962. Shachter, R. D. (1986) Evaluating influence diagrams. Ops Res., 34, 871–882. Discussion on the paper by Murphy Elja Arjas .University of Helsinki/ If I am right the burning issue in social and educational programmes, of which the Fast Track study is an example, is not how to determine an individually optimal dynamic treatment regime but, rather, how a limited total resource should be optimally shared between those who need help. Treatments cannot then be assigned purely on the basis of individual status and treatment histories. The situation is different in clini- cal trials, where the availability of drugs is not a problem but where they often have unwanted side-effects. The simple logic ‘more is better’ does not then necessarily apply. My second comment deals with the concept of potential outcomes and the no unmeasured confounders postulate. I understand that it is tempting to postulate the existence of individual potential outcomes that are indexed by a list of treatments received, the main advantage being that the appealing concept of an ‘individual causal effect’ associated with one regime versus another can then be defined directly as the contrast between the corresponding potential outcomes. But I have great difficulty in understanding what the postulate of no unmeasured confounders, as formulated in the paper, would mean in a concrete study.
    [Show full text]
  • Subgroup Identification and Variable Selection from Randomized Clinical
    Subgroup Identification and Variable Selection from Randomized Clinical Trial Data by Jared C. Foster A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Biostatistics) in The University of Michigan 2013 Doctoral Committee: Professor Bin Nan, Co-chair Professor Jeremy M.G. Taylor, Co-chair Assistant Professor Lu Wang Professor Ji Zhu c Jared C. Foster 2013 All Rights Reserved For my wonderful family and beautiful fianc´e. I couldn't have done this without you. ii ACKNOWLEDGMENTS Thank you to Professors Jeremy Taylor, Bin Nan, Ji Zhu and Lu Wang, my dissertation committee, for all the very helpful questions and comments you have given me. Beyond this, I'd like to thank Jeremy and Bin, my co-advisors, for all the hours they spent meeting with me and all the useful advice they have given me. Bin, you introduced me to the incredibly fascinating field of variable selection, for which I am truly grateful. Jeremy, you introduced me to subgroup analysis and personalized medicine, which, combined with variable selection, has become my true passion. Additionally, despite my best efforts to become your most annoying student, you have been incredibly patient, thoughtfully responding to my many emails and discussing many ideas and issues with me whenever I randomly drop by your office. You guys are awesome, and I am truly blessed to have had the opportunity to learn from both of you. Thank you also to my office mates, past and present, for putting up with me and my love of conversation. Yong-Seok Park, Rebecca Andridge and Connie Lee were incredibly helpful when I was preparing for the qualifying exam.
    [Show full text]
  • A Sample Size Calculator for SMART Pilot Studies
    A sample size calculator for SMART pilot studies Abstract In clinical practice, as well as in other areas where interventions are provided, a sequen- tial individualized approach to treatment is often necessary, whereby each treatment is adapted based on the object0s response. An adaptive intervention is a sequence of decision rules which formalizes the provision of treatment at critical decision points in the care of an individual. In order to inform the development of an adaptive intervention, scientists are increasingly inter- ested in the use of sequential multiple assignment randomized trials (SMART ), which is a type of multi-stage randomized trial where individuals are randomized repeatedly at critical decision points to a set treatment options. While there is great interest in the use of SMART and in the development of adaptive interventions, both are relatively new to the medical and behavioral sciences. As a result, many clinical researchers will first implement a SMART pilot study (i.e., a small-scale version of a SMART ) to examine feasibility and acceptability considerations prior to conducting a full-scale SMART study. A primary aim of this paper is to introduce a new methodology to calculate minimal sample size necessary for conducting a SMART pilot. 1 1 Introduction In the medical and behavioral health sciences, researchers have successfully established evidence-based treatments for a variety of health disorders. However, even with evidence-based treatments, there is heterogeneity in the type of individuals who respond and do not respond to treatment. Treatment effects may also vary over time (within the same individual): a treatment that improves outcomes in the short-run for an individual may not improve outcomes longer-term.
    [Show full text]